In the last three decades, the wireless cellular market has experienced increasing number of subscribers worldwide as well as demand for better services shifting from voice to web-browsing and real-time HD video streaming. This increasing demand for services that requires higher data rate, lower latency and improved reliability has driven a radical evolution of wireless technologies through different standards. Beginning from the first generation analog AMPS and TACS (for voice service) in the early 1980s, to 2G and 2.5G digital GSM, IS-95 and CPRS (for voice and data services) in the 1990s, to 3G with UMTS and CDMA2000 (for web-browsing) in the early 2000s, and finally LTE (for high-speed internet connectivity) currently under deployment in different countries worldwide.
Long-term evolution (LTE) is the standard developed by the 3rd generation partnership project (3GPP) for fourth generation (4G) wireless cellular systems. LTE can achieve theoretically up to 4× improvement in downlink spectral efficiency over previous 3G and HSPA+ standards by exploiting the spatial components of wireless channels via multiple-input multiple-output (MIMO) technology. LTE-Advanced is the evolution of LTE, currently under standardization, that will enable theoretically up to 8× increase in spectral efficiency over 3G standard systems.
Despite this technology evolution, it is very likely that in the next three years wireless carriers will not be able to satisfy the growing demand for data rate due to raising market penetration of smartphones and tables, offering more data-hungry applications like real-time HD video streaming, video conferencing and gaming. It has been estimated that capacity of wireless networks will grow 5× in Europe from 2011 to 2015 due to improved technologies such as LTE as well as more spectrum made available by the government [25]. For example, the FCC is planning to free 500 MHz of spectrum by 2020 (of which 300 MHz will be available by 2015) to promote wireless Internet connectivity throughout the US as part of the National Broadband Plan [24]. Unfortunately, the forecast for capacity usage by 2015 is 23× over 2011 in Europe [25] and similar spectrum deficit is expected to happen in the US by 2014 [26-27]. As a result of this data crunch, revenues for wireless carriers may drop below their CAPEX and OPEX with potentially devastating impact on the wireless market [28].
As capacity gains offered by LTE deployment and increased spectrum availability are insufficient, the only foreseeable solution to prevent this upcoming spectrum crisis is to promote new wireless technologies [29]. LTE-Advanced (the evolution of LTE standard) promises additional gains over LTE through more sophisticated MIMO techniques and by increasing the density of “small cells” [30]. However, there are limits to the number of cells that can fit a certain area without incurring interference issues or increasing the complexity of the backhaul to allow coordination across cells.
One promising technology that will provide orders of magnitude increase in spectral efficiency over wireless links without the limitations of conventional cellular systems is distributed-input distributed-output (DIDO) technology (see Related Patents and Applications as described above. The present invention describes DIDO technology employed in the context of cellular systems (such as LTE or LTE-Advanced), both within and without the constraints of cellular standards, to provide significant performance benefits over conventional wireless systems. We begin with an overview on MIMO and review different spatial processing techniques employed by LTE and LTE-Advanced. Then we show how the present invention provides significant capacity gains for next generation wireless communications systems compared to prior art approaches.
MIMO employs multiple antennas at the transmitter and receiver sides of the wireless link and uses spatial processing to improve link reliability via diversity techniques (i.e., diversity gain) or provide higher data rate via multiplexing schemes (i.e., multiplexing gain) [1-2]. Diversity gain is a measure of enhanced robustness to signal fading, resulting in higher signal-to-noise ratio (SNR) for fixed data rate. Multiplexing gain is obtained by exploiting additional spatial degrees of freedom of the wireless channel to increase data rate for fixed probability of error. Fundamental tradeoffs between diversity and multiplexing in MIMO systems were described in [3-4].
In practical MIMO systems, link adaptation techniques can be used to switch dynamically between diversity and multiplexing schemes based on propagation conditions [20-23]. For example, link adaptation schemes described in [22-23] showed that beamforming or Orthogonal Space-Time Block Codes (OSTBC) are preferred schemes in low SNR regime or channels characterized by low spatial selectivity. By contrast, spatial multiplexing can provide significant gain in data rate for channels with high SNR and high spatial selectivity. For example, FIG. 1 shows that cells can be divided in two regions: i) multiplexing region 101, characterized by high SNR (due to proximity to the cell tower or base station) where the spatial degrees of freedom of the channel can be exploited via spatial multiplexing to increase data rate; ii) diversity region 102 or cell-edge, where spatial multiplexing techniques are not as effective and diversity methods can be used to improve SNR and coverage (yielding only marginal increase in data rate). Note that the circle of the macrocell 103 in FIG. 1 labels the shaded center of the circle as the “multiplexing region” and the unshaded outer region of the circle as the “diversity region”. This same region designation is used throughout FIGS. 1, 3-5, where the shaded region is the “multiplexing region” and the unshaded region is the “diversity region”, even if they are not labeled. For example, the same designation is used for the small-cell 104 in FIG. 1.
The LTE (Release 8) and LTE-Advanced (Release 10) standards define a set of ten transmission modes (TM) including either diversity or multiplexing schemes [35,85-86]:                Mode 1: Single antenna port, port 0        Mode 2: Transmit diversity        Mode 3: Large-delay cyclic delay diversity (CDD), extension of open-loop spatial multiplexing for single-user MIMO (SU-MIMO)        Mode 4: Closed-loop spatial multiplexing for SU-MIMO        Mode 5: Multi-user MIMO (MU-MIMO)        Mode 6: Closed-loop spatial multiplexing, using a single transmission layer        Mode 7: Single antenna port, UE-specific RS (port 5)        Mode 8: Single or dual-layer transmission with UE-specific RS (ports 7 and/or 8)        Mode 9: Single or up to eight layers closed-loop SU-MIMO (added in Release 10)        Mode 10: Multi-layer closed-loop SU-MIMO, up to eight layers (added in Release 10)        
Hereafter we describe diversity and multiplexing schemes commonly used in cellular systems as well as specific methods employed in LTE as outlined above, and compare them against techniques that are unique for DIDO communications. We first identify two types of transmission methods: i) intra-cell methods (exploiting micro-diversity in cellular systems), using multiple antennas to improve link reliability or data rate within one cell; ii) inter-cell methods (exploiting macro-diversity), allowing cooperation between cells to provide additional diversity or multiplexing gains. Then we describe how the present invention provides significant advantages (including spectral capacity gain) over prior art.
1. Intra-cell Diversity Methods
Intra-cell diversity methods operate within one cell and are designed to increase SNR in scenarios with poor link quality (e.g., users at the cell-edge subject to high pathloss from the central tower or base station). Typical diversity schemes employed in MIMO communications are beamforming [5-11] and orthogonal space-time block codes (OSTBC) [12-15].
Diversity techniques supported by the LTE standard are transmit diversity, closed-loop rank-1 precoding and dedicated beamforming [31-35]. Transmit diversity scheme supports two or four transmit antennas over the downlink (DL) and only two antennas for the uplink (UL). In the DL channel, it is implemented via space-frequency block codes (SFBC) combined with frequency-switched transmit diversity (FSTD) to exploit space as well as frequency selectivity [31]. Rank-1 precoding creates a dedicated beam to one user based on quantized weights selected from a codebook (pre-designed using limited feedback techniques [36-42]) to reduce the feedback overhead from the user equipment (UE) to the base transceiver station (BTS 105 in FIG. 1, or eNodeB using LTE terminology). Alternatively, dedicated beamforming weights can be computed based on UE-specific reference signal.
2. Intra-cell Multiplexing Methods
MIMO multiplexing schemes [1,19] provide gain in data rate in high SNR regime and in scenarios with enough spatial degrees of freedom in the channel (e.g., rich multipath environments with high spatial selectivity [16-18]) to support multiple parallel data streams over wireless links.
The LTE standard supports different multiplexing techniques for single-user MIMO (SU-MIMO) and multi-user MIMO (MU-MIMO) [31]. SU-MIMO schemes have two modes of operation: i) closed-loop, exploiting feedback information from the UE to select the DL precoding weights; ii) open-loop, used when feedback from the UE is unavailable or the UE is moving too fast to support closed-loop schemes. Closed-loop schemes use a set of pre-computed weights selected from a codebook. These weights can support two or four transmit antennas as well as one to four parallel data streams (identified by number of layers of the precoding matrix), depending on the UE request and decision of the scheduler at the BTS. LTE-Advanced will include new transmission modes up to MIMO 8×8 to provide up to 8× increase in spectral efficiency via spatial processing [62].
MU-MIMO schemes are defined for both UL and DL channels [31,50]. In the UL, every UE sends a reference signal to the BTS (consisting of cyclically shifted version of the Zadoff-Chu sequence [33]). Those reference signals are orthogonal, such that the BTS can estimate the channel from all UEs and demodulate data streams from multiple UEs simultaneously via spatial processing. In the DL, precoding weights for different UEs are selected from codebooks based on the feedback from the UEs and the scheduler (similarly to closed-loop SU-MIMO schemes) and only rank-1 precoding is allowed for every UE (e.g., each UE receives only one data stream).
Intra-cell multiplexing techniques employing spatial processing provide satisfactory performance only in propagation scenarios characterized by high SNR (or SINR) and high spatial selectivity (multipath-rich environments). For conventional macrocells, these conditions may be harder to achieve as BTSs are typically far from the UEs and the distribution of the SINR is typically centered at low values [43]. In these scenarios, MU-MIMO schemes or diversity techniques may be better choices than SU-MIMO with spatial multiplexing.
Other techniques and network solutions contemplated by LTE-Advanced to achieve additional multiplexing gain (without requiring spatial processing through MIMO) are: carrier aggregation (CA) and small cells. CA [30,44-47] combines different portions of the RF spectrum to increase signal bandwidth up to 100 MHz [85], thereby yielding higher data rates. Intra-band CA combines different bands within the same portion of the spectrum. As such it can use the same RF chain for multiple channels, and multiple data streams are recombined in software. Inter-band CA requires different RF chains to operate at different portions of the spectrum as well as signal processing to recombine multiple data streams from different bands.
The key idea of small cells [30,47] is to reduce the size of conventional macro-cells, thereby allowing higher cell density and larger throughput per area of coverage. Small-cells are typically deployed through inexpensive access points 106 with low power transmission (as depicted in FIG. 1) as opposed to tall and expensive cell towers used for macro-cells. Two types of small cells are defined in LTE-Advanced: i) metrocells, for outdoor installation in urban areas, supporting up 32 to 64 simultaneous users; and ii) femtocells, for indoor use, can serve at most 4 active users. One advantage of small cells is that the density of UEs close to the BTS is statistically higher, yielding better SNR that can be exploited via spatial multiplexing to increase data rate. There are, however, still many concerns about practical deployment of small cells, particularly related to the backhaul. In fact, it may be challenging to reach BTSs of every small cell via high-speed wireline connections, especially considering the high density of metrocells and femtocells in a given coverage area. While using Line-Of-Sight (LOS) backhaul to small cells can often be implemented inexpensively, compared to wireline backhaul, there often are no practical LOS backhaul paths available for preferred small cell BTS placements, and there is no general solution for Non-Line-Of-Sight (NLOS) wireless backhaul to small cell BTSs. Moreover, small cells require complex real-time coordination across BTSs to avoid interference as in self-organized networks (SON) [30,51-52] and sophisticated cell-planning tools (even more complex than conventional cellular systems, due to higher density of small cells) to plan their optimal location [48,49]. Finally, handoff is a limiting factor for small cells deployment, particularly in scenarios where groups of subscribers switch cells at the same time, causing large amount of handoff overhead over the backhaul, resulting in high latency and unavoidable dropped calls.
It can be trivially shown there is no practical general solution that enables small cells to co-exist with macrocells and achieve optimal, or necessarily even improved, throughput. Among the myriad of such unsolvable situations is when a small cell is located such that its UEs unavoidably overlap with a macrocell transmission and the small cell and the macrocell use the same frequencies to reach their respective UEs. Clearly in this situation, the macrocell transmission will interfere with the small cell transmission. While there may be some approach that mitigates such interference for particular circumstances of a particular macrocell, a particular small cell, the particular macrocell and small cell UEs involved, the throughput requirements of those UEs, and environmental circumstances, etc., any such approach would be highly specific, not only to the static plan of the macrocell and small cell, but to the dynamic circumstances of a particular time interval. Typically, the full throughput of the channel to each UE cannot be achieved.
3. Inter-cell Diversity Methods
In a heterogeneous network (HetNet) [90] where macro-cells coexist with small-cells (e.g., metro-cells, pico-cells and femto-cells) it is necessary to employ different techniques to eliminate inter-cell interference. While HetNets provide better coverage through small-cells, the gains in data rate are only marginal since they require sharing the spectrum through different forms of frequency reuse patterns or using spatial processing to remove interference rather than achieve multiplexing gain. The LTE standards employ inter-cell interference coordination (ICIC) schemes to remove interference particularly at the cell-edge. There are two types of ICIC methods: cell-autonomous and coordinated between BTSs.
Cell-autonomous ICIC schemes avoid inter-cell interference via different frequency reuse patterns depicted in FIG. 2, where the hexagons represent the cells and the colors refer to different carrier frequencies. Three types of schemes are considered in LTE: i) full frequency reuse (or reuse 1), where the cells utilize all the available bandwidth as in FIG. 2a, thereby producing high interference at the cell-edge; ii) hard frequency reuse (HFR), where every cell is assigned with a different frequency band as in FIG. 2b (with typical reuse factor of 3) to avoid interference across adjacent cells; iii) fractional frequency reuse (FFR), where the center of the cell is assigned with the whole available bandwidth as in frequency reuse 1, whereas the cell-edge operates in HFR mode to mitigate inter-cell interference as in FIG. 2c. 
Coordinated ICIC methods enable cooperation across BTSs to improve performance of wireless networks. These techniques are a special case of methods taught in Related Patents and Applications as described above to enable cooperation across wireless transceivers in the general case of distributed antenna networks for multiple UEs all using the same frequency simultaneously. Cooperation across BTSs to remove inter-cell interference for the particular case of cellular systems for a single UE at a given time at a given frequency was described in [53]. The system in [53] divides every macrocell into multiple subcells and enables soft-handoff across subcells by employing dedicated beamforming from coordinated BTSs to improve link robustness at a single UE at a single frequency, as it moves along the subcell boundaries.
More recently, this class of cooperative wireless cellular networks has been defined in the MIMO literature as “network MIMO” or “coordinated multi-point” (CoMP) systems. Theoretical analysis and simulated results on the benefits obtained in network MIMO by eliminating inter-cell interference are presented in [54-61]. The key advantage of network MIMO and CoMP is to remove inter-cell interference in the overlapping regions of the cells denoted as “interference region” 301 in FIG. 3 for the case of macro-cells 302.
CoMP networks are actively becoming part of LTE-Advanced standard as a solution to mitigate inter-cell interference in next generation cellular networks [62-64]. Three CoMP solutions have been proposed so far in the standard to remove inter-cell interference: i) coordinated scheduling/beamforming (CS/CB), where the UE receives its data stream from only one BTS via beamfoming and coordination across BTSs is enabled to remove interference via beamforming or scheduling techniques; ii) dynamic cell selection (DCS) that chooses dynamically the cell for every UE on a per-subframe basis, transparently to the UE; iii) joint transmission (JT), where data for given UE is jointly transmitted from multiple BTSs to improve received signal quality and eliminate inter-cell interference. CoMP-JT yields larger gains than CoMP-CS/CB at the expenses of higher overhead in the backhaul to enable coordination across BTSs.
4. Inter-cell Multiplexing Methods
Prior art multi-user wireless systems add complexity and introduce limitations to wireless networks which result in a situation where a given user's experience (e.g. available throughput, latency, predictability, reliability) is impacted by the utilization of the spectrum by other users in the area. Given the increasing demands for aggregate throughput within wireless spectrum shared by multiple users, and the increasing growth of applications that can rely upon multi-user wireless network reliability, predictability and low latency for a given user, it is apparent that prior art multi-user wireless technology suffers from many limitations. Indeed, with the limited availability of spectrum suitable for particular types of wireless communications (e.g. at wavelengths that are efficient in penetrating building walls), prior art wireless techniques will be insufficient to meet the increasing demands for bandwidth that is reliable, predictable and low-latency.
Prior art intra-cell diversity and multiplexing methods can only provide up to a theoretical 4× increase in throughput over current cellular networks for LTE (through MIMO 4×4) or at most a theoretical 8× for LTE-Advanced (through MIMO 8×8), although higher orders of MIMO achieve diminishing improvements in increasing throughput in a given multipath environment, particularly as UEs (such as smartphones) get smaller and more constrained in terms of antenna placement. Other marginal throughput gains in next generation cellular systems may be obtained from additional spectrum allocation (e.g., FCC national broadband plan), exploited via carrier aggregation techniques, and more dense distribution of BTSs via small cell networks and SON [30,46]. All the above techniques, however, still rely heavily on spectrum or time sharing techniques to enable multi-user transmissions, since the spectral efficiency gains obtained by spatial processing is limited.
While prior art inter-cell methods (e.g., network MIMO and CoMP systems [53-64]) can improve reliability of cellular networks by eliminating inter-cell interference, their capacity gains are only marginal. In fact, those systems constrain power transmitted from every BTS to be contained within the cell boundaries and are only effective to eliminate inter-cell interference due to power leakage across cells. FIG. 3 shows one example of cellular networks with three BTSs, each one characterized by its own coverage area or cell. The power transmitted from each BTS is constrained to limit the amount of interference across cells, depicted in FIG. 3 by the areas where the cells overlap. As these systems operate in the low SINR regime at the interference region, their gains in spectral efficiency is only marginal, similarly to intra-cell schemes for SU-MIMO. To truly obtain significant capacity gains in inter-cell cooperative networks, power constraints limited to cell-boundaries must be relaxed and spatial multiplexing techniques should be enabled throughout the cells where the SINR is high (not just at the cell-edge with poor SINR performance as in prior art approaches).
FIG. 4 shows the case where the power transmitted from the three BTSs 401 all transmitting simultaneously at the same frequency is increased, thereby allowing a higher level of interference throughout the cell 402. In prior art systems, such interference would result in incoherent interference (disrupting UE signal reception) throughout the interfering areas of the BTSs, but this interference is actually exploited in the present invention through novel inter-cell multiplexing methods using spatial processing to create areas of coherent interference (enhancing UE signal reception) around every UE, thereby providing simultaneous non-interfering data streams to every UE and increasing their SINR throughout the cell.
The scenario depicted in FIG. 4 is described in [89] for the particular case of cellular systems. The system in [89] consists of several BTSs identifying different cells that are grouped into clusters. Cooperation is allowed only across BTSs from adjacent cells within the same clusters. In this case it was shown that, as the power transmitted from the BTSs increases, there is a limit to the capacity (or spectral efficiency) achievable through inter-cell multiplexing methods. In fact, as the transmit power increases, the out-of-cluster interference increases proportionally, producing a saturation regime for the SINR and consequently for the capacity. As a consequence of this effect, the system in [89] can theoretically achieve at most 3× gain in capacity (i.e., at most three cells within the cluster) and any additional cell included in the cluster would reduce capacity due to increased out-of-cluster interference (e.g., the case of 21 cells per cluster yields lower capacity than the case of 3 cells per cluster). We observe that the fundamental capacity limit in [89] holds because the BTSs are constrained to predefined locations, as in cellular systems, and multiplexing gain is achieved by increasing transmit power from the BTSs. To obtain theoretically unlimited capacity gain via inter-cell multiplexing methods, the constraint on the BTS placement must be removed, allowing the BTSs to be placed anywhere is convenient.
It would thus be desirable to provide a system that achieves orders of magnitudes increase in spectral efficiency exploiting inter-cell multiplexing gain via spatial processing by removing any constraint on the power transmitted from distributed BSTSs 501 as well as on their placement. FIG. 5 shows one example where many additional access points 502 are added to deliberately increase the level of incoherent interference throughout the cell 503, that is exploited in the present invention to generate areas of coherent interference around UEs, thereby yielding theoretically unlimited inter-cell multiplexing gain. The additional access points are placed serendipitously wherever it is convenient and are not constrained to any specific cell planning, as in cellular systems described in prior art. In an exemplary embodiment of the invention, the serendipitous access points are distributed-input distributed-output (DIDO) access points and the inter-cell multiplexing gain is achieved through DIDO methods as described above and [77-78]. In another embodiment, the serendipitous access points are low power transceivers, similar to inexpensive Wi-Fi access points or small-cells [30,47], thereby providing smaller areas of coverage overlapping throughout the macro-cell as shown in FIG. 5.
We observe that prior art inter-cell methods [53-64] avoid incoherent interference by intentionally limiting the transmit power from every BTS as in FIG. 3 and eliminate residual inter-cell interference (on the overlapping areas between cells) via spatial processing, thereby providing improved SINR and inter-cell diversity gain. We further observe that [89] constrains BTS placement to cell planning while increasing transmit power, thereby limiting the achievable capacity due to out-of-cluster interference, and as such it is still limited by interference. By contrast, the present invention exploits incoherent interference to create coherent interference around the UEs, by transmitting higher power from every BTS serendipitously placed, thereby improving signal quality at the UE that is necessary condition to obtain inter-cell multiplexing gain throughout the cell via spatial processing. As such, the systems described in prior art cannot be used to achieve unlimited inter-cell multiplexing gain via spatial processing, since there is not sufficient SINR throughout the cell (due to the limited transmit power from the BTSs or the out-of-cluster interference when transmit power is increased) to enable inter-cell multiplexing methods as in the present invention. Moreover, the systems described in prior art would be inoperable to achieve the multiplexing gain achieved in the present invention depicted in FIGS. 4-5, given that prior art systems were designed to avoid inter-cell interference within the diversity regions shown in the shaded area of FIG. 1 and FIGS. 3-5 rather than exploit inter-cell interference in the multiplexing regions to obtain inter-cell multiplexing gain as achieved in the present invention.